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LiDAR Data Classification Based on Improved Conditional Generative Adversarial Networks
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DOI：10.1109/ACCESS.2020.3039211.png)
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Abstract
En 中文
Light detection and ranging (LiDAR) data contains the height of different objects and records the elevation information of ground objects, so it plays an important role in land classification. In recent years, deep learning has been widely used in LiDAR data classification due to its strong ability to extract features. However, deep learning methods usually need sufficient training data to achieve better classification results. In order to solve this problem, a new classification method combined conditional generative adversarial network (CGAN) with residual unit and DropBlock, is proposed here for the classification of LiDAR data, called as RDB-CGAN. CGAN expands the generated samples to training data to improve the classification performance when the training samples are relatively small. Residual unit increases the network depth of the generator to improve its generation capability and utilizes shortcut connection to transfer the input information directly to the output to solve degradation caused by increased network depth. DropBlock improved the generalization of the network by dropping a whole area with spatial information correlation so that the network can learn the remaining features. The experimental results on two different LiDAR datasets show that RDB-CGAN significantly improved the classification performance of LiDAR data compared to several state-of-the-art classification methods.
Keywords:
Laser radar
Generative adversarial networks
Training
Generators
Feature extraction
Data models
Hyperspectral imaging
Data classification
light detection and ranging (LiDAR)
conditional generative adversarial network (CGAN)
residual unit
DropBlock
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